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LangGraph agents use checkpointing to save state during execution. The platform saves a snapshot of the graph state at every super-step, so conversations survive failures and restarts. For more details, see the LangGraph checkpointing persistence documentation.

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What checkpointing provides

Checkpoints are saved to a thread, identified by a unique thread_id. This enables:
  • Memory: Maintain context between interactions in conversations
  • Human-in-the-loop: Inspect, interrupt, and approve graph steps
  • Time travel: Replay prior executions and debug specific steps
  • Fault tolerance: Resume from the last successful step after failures

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Set up checkpointing

1

Navigate to the checkpointing step

During agent creation or editing, navigate to the Checkpointing step in the agent form.
2

Choose a backend

Select the checkpointing backend that matches your deployment needs. See the sections below for details on each option.
3

Fill in connection details

Enter the required configuration for your chosen backend (file path for SQLite, connection string for PostgreSQL).
4

Save and restart

Click Next to continue, then finalize with Save changes. Restart the agent to apply the new configuration.

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Checkpointing backends

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In-memory

The InMemorySaver stores checkpoints in the application’s memory. This is the default option with no external dependencies.
PropertyDetail
PersistenceNone. Data is lost when the application restarts.
PerformanceFastest option, no I/O overhead.
Use casesDevelopment, testing, stateless workflows.
Configuration: No additional configuration required.

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SQLite

The SqliteSaver uses a file-based SQLite database to store checkpoints.
PropertyDetail
PersistenceData persists on disk in a single file.
PerformanceFast for single-process applications.
ConcurrencyLimited to single-writer scenarios.
Use casesLocal development, small-scale applications.
Configuration: Requires a database file path. 
agent:
  type: "LANGGRAPH"
  config:
    checkpointer:
      type: "sqlite"
      db_url: "checkpoints.db"

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PostgreSQL

The PostgresSaver uses PostgreSQL for checkpoint storage. This is the recommended backend for production deployments.
PropertyDetail
PersistenceProduction-grade database storage.
PerformanceOptimized for multi-process and concurrent access.
ScalabilitySupports multiple agent instances.
Use casesProduction deployments, multi-instance setups.
Configuration: Requires a PostgreSQL connection string. 
agent:
  type: "LANGGRAPH"
  config:
    checkpointer:
      type: "postgres"
      db_url: "postgresql://user:pass@localhost:5432/dbname"

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Threads and state

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Threads

A thread is a unique identifier (thread_id) that groups related checkpoints together. Each conversation or interaction should use a unique thread_id to maintain isolation between sessions. When invoking a graph with a checkpointer, you must specify a thread_id: 
config = {"configurable": {"thread_id": "user-123-session-1"}}

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State snapshots

Each checkpoint contains a StateSnapshot with:
  • values: The state channel values at that point in time
  • config: Configuration associated with the checkpoint
  • metadata: Additional metadata about the checkpoint
  • next: Nodes to execute next in the graph
You can retrieve the latest state or a specific checkpoint using graph.get_state(config).

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Best practices

  • Use SQLite for local development: No server setup required, file-based storage
  • Use PostgreSQL for production: Multi-process support, reliability, and scalability
  • Use in-memory for testing: Fastest option for stateless testing scenarios
  • Configure thread isolation: Each conversation should have a unique thread_id
  • Monitor checkpoint storage: Long-running conversations can accumulate significant state

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Troubleshooting

  1. Verify database connection: Test the connection string independently before configuring
  2. Check permissions: Confirm the agent has read/write access to the database or file system
  3. Thread ID required: Always provide a thread_id in the config when using checkpointers
  4. Database schema: PostgreSQL checkpointers automatically create required tables on first use
  5. Review logs: Check agent logs for checkpoint-related errors
Last modified on March 22, 2026